A decision support system (DSS) is an information system that supports decision-making activities.
This post is an introduction to decision support system (DSS).
Decision Support System Classification
There are different DSS taxonmies:
- Haettenschwiller’s relationship to the user taxonomy
- D. Power’s mode of assistance taxonomy
Haettenschwiler’s Relationship to the User Taxonomy
Haettenschwiler’s classification of DSS:
- Passive
- Active
- Communication
Passive assists decision-making by providing relevant data but do not suggest or recommend decisions. Example: A reporting tool that compiles historical sales data.
Active goes beyond data presentation and actively provide recommendations or solutions based on analysis.
Example: An AI-powered forecasting system that suggests inventory levels.
Communication facilitates collaboration and communication among decision-makers to enhance group decision-making.
Example: A shared dashboard or groupware for collaborative planning.
D. Power’s Mode of Assistance Taxonomy
D. Power’s classification of DSS:
- Communication-driven
- Data-driven
- Document-driven
- Knowledge-driven
- Model-driven
A communication-driven DSS enables cooperation, supporting a shared tasks; examples include Google Docs or Microsoft SharePoint.
A data-driven DSS emphasizes access to and manipulation of a time series of internal data and sometimes external.
A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.
A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures or in similar structures like interactive decision trees and flowcharts.
A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model.
Criteria Weighting
Criteria weighting methods are used in decision-making and multi-criteria analysis to assign importance to different factors.
Criteria Weighting Methods
These techniques help improve the objectivity and accuracy of decision-making processes.
Criteria weighting methods:
- Direct
- Delphi
- Indirect
- Relative utilities
- Analytic hierarchy process
- Entropy
Delphi
Delphi method or estimate-talk-estimate (ETE) relies on expert consensus through iterative surveys to establish the importance of criteria.
Analytic hierarchy process
Analytic hierarchy process (AHP), also called Saaty because of his developer, is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology.
It was developed by Thomas L. Saaty in the 1970s; Saaty partnered with Ernest Forman to develop Expert Choice software in 1983, and AHP has been extensively studied and refined since then.
Analytic hierarchy process at Wikipedia
Relative Utilities
Relative utilities method evaluates criteria based on their relative contribution to an objective.
Entropy
Entropy method, that it may be the same as entropy weighting method (EWM), measures the amount of information each criterion provides to determine its weight.
Score Normalization
Score normalization methods:
- Fracción de la suma
Criteria Sorting
Criteria sorting methods:
- Método lexicográfico
- Métodos de relaciones de superación
- Método ELECTRE
- Método Promethee
- Concordancia
- Método basado en la de ponderación lineal
- Ponderación lineal
- Utilidad multiatributo
- Topsis o programación compromiso
The public sector in Spain usually uses the lineal pondering method.
Sistema de Soporte a la Decisión (SSP) was developed by the Spain public sector is 2012.
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External references
- Wikipedia community; “Decision support system“; Wikipedia